@book{Numpy,
address = {USA},
author = {Oliphant, Travis E},
edition = {2nd},
isbn = {151730007X, 9781517300074},
publisher = {CreateSpace Independent Publishing Platform},
title = {{Guide to NumPy}},
year = {2015}
}

@misc{SciPy,
annote = {[Online; accessed ]},
author = {Jones, Eric and Oliphant, Travis and Peterson, Pearu and Others},
title = {{SciPy: Open source scientific tools for Python}},
url = {http://www.scipy.org/}
}

@article{Gursoy2014,
abstract = {Analysis of tomographic datasets at synchrotron light sources (including X-ray transmission tomography, X-ray fluorescence microscopy and X-ray diffraction tomography) is becoming progressively more challenging due to the increasing data acquisition rates that new technologies in X-ray sources and detectors enable. The next generation of synchrotron facilities that are currently under design or construction throughout the world will provide diffraction-limited X-ray sources and are expected to boost the current data rates by several orders of magnitude, stressing the need for the development and integration of efficient analysis tools. Here an attempt to provide a collaborative framework for the analysis of synchrotron tomographic data that has the potential to unify the effort of different facilities and beamlines performing similar tasks is described in detail. The proposed Python-based framework is open-source, platform- and data-format-independent, has multiprocessing capability and supports procedural programming that many researchers prefer. This collaborative platform could affect all major synchrotron facilities where new effort is now dedicated to developing new tools that can be deployed at the facility for real-time processing, as well as distributed to users for off-site data processing.},
author = {G{\"{u}}rsoy, Doǧa and {De Carlo}, Francesco and Xiao, Xianghui and Jacobsen, Chris},
doi = {10.1107/S1600577514013939},
file = {:home/sindreno/Downloads/s-21-01188.pdf:pdf},
issn = {16005775},
journal = {Journal of Synchrotron Radiation},
keywords = {X-ray imaging,phase retrieval,tomography},
number = {5},
pages = {1188--1193},
title = {{TomoPy: A framework for the analysis of synchrotron tomographic data}},
volume = {21},
year = {2014}
}

@article{Pelt2016,
abstract = {The processing of tomographic synchrotron data requires advanced and efficient software to be able to produce accurate results in reasonable time. In this paper, the integration of two software toolboxes, TomoPy and the ASTRA toolbox, which, together, provide a powerful framework for processing tomographic data, is presented. The integration combines the advantages of both toolboxes, such as the user-friendliness and CPU-efficient methods of TomoPy and the flexibility and optimized GPU-based reconstruction methods of the ASTRA toolbox. It is shown that both toolboxes can be easily installed and used together, requiring only minor changes to existing TomoPy scripts. Furthermore, it is shown that the efficient GPU-based reconstruction methods of the ASTRA toolbox can significantly decrease the time needed to reconstruct large datasets, and that advanced reconstruction methods can improve reconstruction quality compared with TomoPy's standard reconstruction method.},
author = {Pelt, Dani{\"{e}}l M. and G{\"{u}}rsoy, Dog'a and Palenstijn, Willem Jan and Sijbers, Jan and {De Carlo}, Francesco and Batenburg, Kees Joost},
doi = {10.1107/S1600577516005658},
file = {:home/sindreno/Downloads/s-23-00842.pdf:pdf},
issn = {16005775},
journal = {Journal of Synchrotron Radiation},
keywords = {ASTRA toolbox,TomoPy,tomography},
number = {3},
pages = {842--849},
title = {{Integration of TomoPy and the ASTRA toolbox for advanced processing and reconstruction of tomographic synchrotron data}},
volume = {23},
year = {2016}
}

@article{vanAarle:16,
author = {Wim van Aarle and Willem Jan Palenstijn and Jeroen Cant and Eline Janssens and Folkert Bleichrodt and Andrei Dabravolski and Jan De Beenhouwer and K. Joost Batenburg and Jan Sijbers},
journal = {Opt. Express},
keywords = {Tomographic image processing; Computational imaging ; Tomographic imaging ; Computed tomography; Detector arrays; Phase retrieval; Radiation detectors; Reconstruction algorithms; X ray computed tomography},
number = {22},
pages = {25129--25147},
publisher = {OSA},
title = {Fast and flexible X-ray tomography using the ASTRA toolbox},
volume = {24},
month = {Oct},
year = {2016},
url = {http://www.opticsexpress.org/abstract.cfm?URI=oe-24-22-25129},
doi = {10.1364/OE.24.025129},
abstract = {Object reconstruction from a series of projection images, such as in computed tomography (CT), is a popular tool in many different application fields. Existing commercial software typically provides sufficiently accurate and convenient-to-use reconstruction tools to the end-user. However, in applications where a non-standard acquisition protocol is used, or where advanced reconstruction methods are required, the standard software tools often are incapable of computing accurate reconstruction images. This article introduces the ASTRA Toolbox. Aimed at researchers across multiple tomographic application fields, the ASTRA Toolbox provides a highly efficient and highly flexible open source set of tools for tomographic projection and reconstruction. The main features of the ASTRA Toolbox are discussed and several use cases are presented.},
}

@article{VANAARLE201535,
title = "The ASTRA Toolbox: A platform for advanced algorithm development in electron tomography",
journal = "Ultramicroscopy",
volume = "157",
pages = "35 - 47",
year = "2015",
issn = "0304-3991",
doi = "10.1016/j.ultramic.2015.05.002",
url = "http://www.sciencedirect.com/science/article/pii/S0304399115001060",
author = "Wim van Aarle and Willem Jan Palenstijn and Jan De Beenhouwer and Thomas Altantzis and Sara Bals and K. Joost Batenburg and Jan Sijbers",
keywords = "Electron tomography, Reconstruction, ASTRA Toolbox, Dual-axis",
abstract = "We present the ASTRA Toolbox as an open platform for 3D image reconstruction in tomography. Most of the software tools that are currently used in electron tomography offer limited flexibility with respect to the geometrical parameters of the acquisition model and the algorithms used for reconstruction. The ASTRA Toolbox provides an extensive set of fast and flexible building blocks that can be used to develop advanced reconstruction algorithms, effectively removing these limitations. We demonstrate this flexibility, the resulting reconstruction quality, and the computational efficiency of this toolbox by a series of experiments, based on experimental dual-axis tilt series."
}

@article{Hickstein2016,
author = {Hickstein, Daniel D. and Yurchak, Roman and Das, Dhrubajyoti and Shih, Chung-You and Gibson, Stephen T.},
doi = {10.5281/ZENODO.47423},
keywords = {Abel Transform,Abel inversion,Data processing,Flame,Image processing,Laser ablation,Laser plume,Photoelectron,Photoion,Physical chemistry,Physics,Python},
month = {mar},
title = {{PyAbel (v0.7): A Python Package for Abel Transforms}},
url = {https://zenodo.org/record/47423},
year = {2016}
}

@article{Feldkamp:84,
author = {L. A. Feldkamp and L. C. Davis and J. W. Kress},
journal = {J. Opt. Soc. Am. A},
keywords = {Destructive interference; Detector arrays; Fourier transforms; Image intensifiers; Three dimensional reconstruction; X ray imaging},
number = {6},
pages = {612--619},
publisher = {OSA},
title = {Practical cone-beam algorithm},
volume = {1},
month = {Jun},
year = {1984},
url = {http://josaa.osa.org/abstract.cfm?URI=josaa-1-6-612},
doi = {10.1364/JOSAA.1.000612},
abstract = {A convolution-backprojection formula is deduced for direct reconstruction of a three-dimensional density function from a set of two-dimensional projections. The formula is approximate but has useful properties, including errors that are relatively small in many practical instances and a form that leads to convenient computation. It reduces to the standard fan-beam formula in the plane that is perpendicular to the axis of rotation and contains the point source. The algorithm is applied to a mathematical phantom as an example of its performance.},
}
